Deinterlacing is an art that involves converting interlaced video fields into non-interlaced video frames. Deinterlacing is required because most modern televisions are inherently progressive and the video feed is broadcasted in interlaced form.
There are three common techniques to deinterlace an interlaced video feed. One of these techniques is known as vertical interpolation. Vertical interpolation involves averaging at least two scan lines to generate a new scan line. The technique is repeated for all scan lines and creates a full frame from a single video field. While vertical interpolation allows a progressive picture to be generated from one video field, half of the resolution of the video feed is lost.
Another deinterlacing technique is “weaving.” Weaving involves merging a video field containing odd scan lines with a subsequent field containing even scan lines. The two fields are combined to generate a single progressive frame. Weaving is beneficial because it preserves the full resolution of the video feed. However, if motion is present, weaving results in motion artifacts because the two fields are not temporally aligned.
A third technique is known as motion adaptive deinterlacing. Motion adaptive is a combination of the vertical interpolation technique and the weaving technique. Motion-adaptive techniques make a pixel-by-pixel determination as to whether motion is present in the local area of the pixel. If motion is detected, then vertical interpolation is used. If no motion is detected, the weaving technique is used. However, when new pixels are calculated from a single field, jagged edges on objects result. Often, the jagged edges are caused by aliasing in the single field since using only every other line does not provide a vertical sampling frequency that is high enough to meet the Nyquist rate. Hence, aliases are present in the single field which are caused by the low vertical sampling rate. These aliases result in unnatural looking, jagged object edges.
In order to reduce the aliasing artifacts, edge detection is used. Edge detection involves detecting the edge location in the image and calculating new pixel values based on known pixels aligned along the edge direction. Using the known pixel values along the detected edge to calculate the new pixel values reduces or eliminates the aliasing artifacts.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.